AI-Driven Data Preparation and Curation for Reservoir Characterization

Reliable reservoir characterization requires high-quality, consistent data, and as machine learning (ML) becomes more embedded in geoscience, robust preparation and curation are essential. This study presents an integrated workflow that combines ML-assisted log QC, reconstruction, and rock typing with automated static reservoir model assessment. Using algorithms such as Isolation Forest, stratigraphy-based clustering, and Random Forest, the approach corrects anomalies, fills missing intervals, and predicts rock properties in uncored wells, enabling more complete and reliable 3D geological models. Automated outputs, including isochore maps, zonal validations, and log-derived comparisons, improve accuracy, highlight inconsistencies, and assess the impact of new well data on model reliability. The workflow reduces manual effort, accelerates project timelines, and increases usable well data by leveraging records previously discarded for poor quality. By using ML with traditional geoscience analysis, this methodology enhances real-time decision-making, optimizes well placement, and improves hydrocarbon recovery, setting a new standard for data-driven reservoir evaluation.